Have U.S. Manufacturing Inventories Really Decreased? An Empirical Study

Rajagopalan, Sampath; Malhotra, Arvind

doi:10.1287/msom.3.1.14.9995

Cited by 123 publications

(93 citation statements)

References 4 publications

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“…Since the department editor information is available only from 2004, we exclude articles published during 2000-2003. 11 The early empirical papers were primarily concerned with examining inventory performance over time (e.g., Rajagopalan and Malhotra 2001;Chen et al 2005Chen et al , 2007. More recently, researchers have shifted toward establishing causal relationships between inventory and other operational variables.…”

Section: Endnotesmentioning

confidence: 99%

OM Forum—Causal Inference Models in Operations Management

Lim

Reza

et al. 2017

M&SOM

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Abstract. Operations management (OM) researchers have traditionally focused on developing normative mathematical models that prescribe what managers and firms should do. Recently, there has been increased interest in understanding what managers and firms actually do and the factors that drive these decisions. To advance this understanding, empirical investigation using causal inference models is critical. However, in many contexts, the ability to obtain causal inferences is fraught with the challenges of endogeneity and selection bias. This paper describes five empirical tools that have been widely used in economics to address these challenges and how they can be adopted by OM researchers. We also present an example that illustrates how the various attributes of big data-variety, velocity, and volume-can be useful in addressing the endogeneity bias.

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Section: Endnotesmentioning

confidence: 99%

OM Forum—Causal Inference Models in Operations Management

Lim

Reza

et al. 2017

M&SOM

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“…We add to this stream of research by linking other factors associated with production and scheduling that are associated with finished-goods inventory. Several papers explore inventory at the industry level with a focus on either the long-run trend in inventory (e.g., Chen et al 2005, Rajagopalan andMalhotra 2001) or the volatility of production relative to sales (e.g., Cachon et al 2007)-we do not consider either of those issues in our study. There is a growing literature that explores firm-level inventory rather than that at the product/model level as we do.…”

Section: Literature Reviewmentioning

confidence: 99%

Drivers of Finished-Goods Inventory in the U.S. Automobile Industry

2010

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A utomobile manufacturers in the U.S. supply chain exhibit significant differences in their days of supply of finished vehicles (average inventory divided by average daily sales rate). For example, from 1995 to 2004, Toyota consistently carried approximately 30 fewer days of supply than General Motors. This suggests that Toyota's well-documented advantage in manufacturing efficiency, product design, and upstream supply chain management extends to their finished-goods inventory in their downstream supply chain from their assembly plants to their dealerships. Our objective in this research is to measure for this industry the effect of several factors on inventory holdings. We find that two factors, the number of dealerships in a manufacturer's distribution network and a manufacturer's production flexibility, explain essentially all of the difference in finished-goods inventory between Toyota and three other manufacturers: Chrysler, Ford, and General Motors.

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“…Rajagopalan and Malhotra (2001) study trends in inventory levels at US firms over time to test the widely held belief that inventory management has improved due to the introduction of just-in-time (JIT) practices and IT system implementations. Using a large sample of firms from the US Census Bureau including both private and public companies, they find that material and work-in-process inventories decreased in the majority of the two-digit SIC industries from 1961 to 1994.…”

Section: Literature Reviewmentioning

confidence: 99%

What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation

Rumyantsev

Netessine

2007

M&SOM

192

237

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Classical inventory models offer a variety of insights into the optimal way to manage inventories of individual products. However, top managers and industry analysts are often concerned with the aggregate macroscopic view of a firm's inventory rather than with the inventories of individual products. Given that classical inventory models often do not account for many practical considerations that a company's management faces (e.g., competition, industry dynamics, business cycles, the financial state of the company and of the economy, etc.) and that they are derived at the product level and not the firm level, can insights from these models be used to explain the inventory dynamics of entire companies? This exploratory study aims to address this issue using empirical data.We analyze absolute and relative inventories using a quarterly data panel that contains 722 public U.S. companies for the period 1992-2002. We have chosen companies that are not widely diversified and whose business in large part relies on inventory management to concentrate on empirically testing hypotheses derived from a variety of classical inventory models (economic order quantity (EOQ), [Q, r], newsvendor, periodic review, etc.). We find empirical evidence that firms operating with more uncertain demand, longer lead times, and higher gross margins have larger inventories. Furthermore, larger companies appear to benefit from economies of scale and therefore have relatively less inventory than smaller companies. We obtain mixed evidence on the relationship between inventory levels and inventory holding costs. We also analyze the breakdown of data into eight segments-oil and gas, electronics, wholesale, retail, machinery, hardware, food, and chemicals-and find that, with a few notable exceptions, our hypotheses are supported within the segments as well. Overall, our results demonstrate that many of the predictions from classical inventory models extend beyond individual products to the aggregate firm level; hence, these models can help with high-level strategic choices in addition to tactical decisions. Abstract: Classical inventory models offer a variety of insights into the optimal way to manage inventories of individual products. However, top managers and industry analysts are often concerned with the aggregate macroscopic view of a firm's inventory rather than with the inventories of individual products. Given that classical inventory models often do not account for many practical considerations that a company's management faces (e.g., competition, industry dynamics, business cycles, the financial state of the company and of the economy, etc.) and that they are derived at the product and not at the firm level, can insights from these models be used to explain the inventory dynamics of entire companies? This exploratory study aims to address this issue using empirical data.We analyze absolute and relative inventories using a quarterly data panel that contains 722 public US companies for the period 1992 to 2002. We have chosen compani...

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Have U.S. Manufacturing Inventories Really Decreased? An Empirical Study

Cited by 123 publications

References 4 publications

OM Forum—Causal Inference Models in Operations Management

OM Forum—Causal Inference Models in Operations Management

Drivers of Finished-Goods Inventory in the U.S. Automobile Industry

What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation

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